Data Ethics Decision Aid (DEDA): a dialogical framework for ethical inquiry of AI and data projects in the Netherlands

Publication date

2021-09

Authors

Franzke, Aline
Muis, IrisORCID 0000-0002-5336-1657ISNI 0000000492528821
Schäfer, Mirko TobiasORCID 0000-0003-0212-7016ISNI 0000000356270811

Editors

Advisors

Supervisors

Document Type

Article
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License

cc_by

Abstract

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present the DEDA framework as an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices.

Keywords

Big data, Data ethics, Data-driven public management, Ethical assessment, Information ethics, IoT, Value-sensitive design, Computer Science Applications, Library and Information Sciences

Citation

Franzke, A, Muis, I M & Schaefer, M T 2021, 'Data Ethics Decision Aid (DEDA): a dialogical framework for ethical inquiry of AI and data projects in the Netherlands', Ethics and Information Technology, vol. 23, no. 3, 3, pp. 551-567. https://doi.org/10.1007/s10676-020-09577-5